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Month: February 2016

This week, we gave the second presentation in class about the plans for our final visualisation.

In this presentation we look at possible ways of visualizing music charts. Our dataset still consists of the Shazam data as described in a previous blog post. During this week we looked into the Spotify API but came to the conclusion there was not more data to be found. We will look into the LastFM API during the upcoming week.

A first visualization is one that visualizes the top 100 of a country. This could then be extended into a second visualization where it is possible to compare the charts of two different countries by selecting them on a map.

Furthermore, a third visualization would allow to select an artist or a certain song and check its popularity over the world. A country where the song/artist ranks higher, would be coloured darker and vice versa.

A possible extension on this visualization would be to include a time aspect, where the popularity over time could be shown.

A few suggestions that were made in class:

include a view to see the same song over different countries

include an easy manner to listen to the songs being visualized

small countries are difficult to spot on a world map, it could be good to scale countries according to popularity

it would be interesting to see how a song goes viral over the world

it could be a possibility to also analyse the lyrics of the song and their mood

perhaps focus on a smaller number of songs and look into more detail for those

These were all very useful suggestions and we are certainly taking them into consideration to improve our work.

Birgit

My InfoVis of this week is this representation of “38 ways to make a perfect coffee”. I think it is ordered in an accessible way. Even without the label reading ‘coffee’ you can tell that that is what it is about. The stylised drawings make it easier to compare the different cups to one another.

I also like the way the ingredients are represented. In my opinion, it is easier to see the amount of an ingredient you have to add as a part of the cup, than as an amount in centiliters – it is harder to imagine how much 8 cl is, then when you say ‘a quarter of a cup’ if you don’t have a measuring cup.

Another aspect of this visualisation is that you can immediately see the type of cup you are supposed to drink a certain coffee from for optimal results. It is much more difficult to describe this in words than with a simple image.

One more thing is that they ordered the types of coffee according to their similarity, which is also nice if you want to change your usual coffee just a little bit, you just look at the image left or right from it.

Carmen

This week I look into a Social Network visualisation.

InMaps is an interactive visual representation of your professional network. The tool is sadly enough not active anymore. The tool creates an interactive visual representation of your professional universe which visualizes the relationships between you and your LinkedIn connections. With it you can better leverage your professional network to help pass along job opportunities, seek professional advice, gather insights, and more.

The map is color-coded to represent different affiliations or groups from your professional career, such as your previous employer, college classmates, or industries you’ve worked in. In this person’s InMap, his LinkedIn colleagues are blue, while former colleagues at Yahoo Analytics are pink and other at Yahoo are green and his Carnegie Mellon classmates are orange and tangerine.

Bigger names represent people who are the most connected within that specific cluster or group. When you click on a contact within a circle you’ll see their profile pop up on the right, as well as lines highlighting how they’re connected to your connections.

Exploration of the map is possible to measure your own impact or influence, or create opportunities for someone else.

One can get a general insight in his network with a first glance by recognizing certain clusters or groups and how these are interconnected. From thereforward, it is possible to explore further. Look into how certain clusters are connected and through who, how certain possible future connections can be made and who are key figures within your network.

Glenn

This week, a visualisation on how long drugs stay in your body using bar charts caught my eye.

It’s is generally considered that bar charts make good visualisations, because we can compare lengths much better than areas or angles [1] (at least when it comes to quantitative data). This is demonstrated above. It’s nice and intuitive to look at it and see the differences. And instead of making it a weird 3D bar chart that combine the blood, urine and hair results into one, they made 3 seperate graphs, each showing their data in a good way.

But there is something wrong with the visualisation as a whole. First, they sorted the data within each graph from small to large, this makes comparison between graphs much more difficult since position is the best way to represent something [1]. So when you want to compare LSD with heroin on all 3 aspects it’s rather hard to do. This can be solved partially by making it interactive or by ordering the drugs alphabetically, so they all have the same position in each graph. Secondly, the scale used on the X-axis is not the same for all 3 graphs. In the first they use hours, probably because the data is more precise and in the last 2 they use days instead, probably an indicator of less accurate data. So to compare the first to the second or third graph, a conversion of the first to days or the latter to hours must be made. This leads to a visual misinterpretation of the data: the first bars are larger than the other but actually represent a shorter time period.

Lastly, there are some things wrong with the individual graphs.
In the first there is one drug that has a lot more hours which would lead to a graph similar to the second graph. But instead of doing that they made the scale non-lineair in the last part, which makes the visualisation less representational.
The last graph on itself doesn’t say much. It just says that each drug can stay in your hair up to 90 days, except for LSD which only stays in your hair up to 3 days. First, the data is very inaccurate which makes a good comparison with the other 2 graphs harder. Secondly, the graph is only useful in comparison to the others due to the lack of variation in the data itself for the third graph.

This week, we gave a first presentation in class about the plans for our final visualisation.

As you can see in the slides, our chosen topic for the visualisation are music charts. For now, we parsed the Shazam website to gather data. Our dataset now consists of the top 100 songs (with artist) for 51 countries and a worldwide ranking.

The main audience we are targeting are music lovers: people who just want to explore what is popular right now, and which music would be interesting to download. Another use could be for a DJ who has a different audience than he is used to (another nationality), who could then quickly explore which music would be interesting to add to his play list.
Another user group could be artists’ managers or artists themselves, who could explore popularity of their songs of competitors’. However, we don’t know how relevant this dataset would be for them. It is clear that we focus on an explorative visualisation.

One of the first visualisation ideas that came to mind is the possiblity to compare charts from different countries by selecting them on a map. Furthermore, you could also select an artist or a certain song and check its popularity worldwide, the darkest colour would then represent the country where the song/artist ranks highest.

Of course, a lot more visualisations are possible and interesting, so this is only the beginning. This is also the main point of feedback that we got from our class mates, together with the suggestion to expand our dataset. We were already looking into expanding it, for example with historical data, and will certainly continue to work on it.

A few suggestions that were made in class:

The mean ranking for a continent would be interesting

The first visualisation in the slides is actually just another way of representing what is on the Shazam website

It would be interesting to show a trend for the top 100, are there any constants, what is the progress over time,…

Visualise which genres are popular in which countries

Compare with data from LastFM, Spotify, iTunes,…

These were all very useful suggestions and we are certainly taking them into consideration to improve our work.

Glenn

This week, a visualisation of the climate change caught my attention.

bron:NASA/GISS

Last month, January, was an abnormally warm one. It broke the record of being the warmest month of January since the recording started. It was 1,13°C warmer than the average month of January and further continues the trend on the record that had been set by December 2015.

The picture above illustrates the difference between January 2016 and an average one. It’s colored according to the scale underneath. The more the color tends to be red, the warmer than usual the temperature was on that location and the more blue, the colder it was. It’s a nice illustration that is quite intuitive. Red is commonly associated with warmth and blue with cold. However if people wanted to know the exact difference between two places, it is not very easy to deduct. The problem comes in 2 stages: (1) getting the exact location of cities on the illustration and (2) getting the exact temperature on that location.
The location can only be dirived from the rough world map that’s drawn in the background, so for a country like Germany it’s hard to see where the red/orange line splits the country and for other inland countries it’s hard to locate them.
The temperature is only given in a color scale which despite being very intuitive, is not accurate (exact). People can’t derive numbers from it, only estimates. And places within the same color can still differ a lot from each other, since red covers 2 points on the scale of Celcius.
The second problem this illustration has, is the scale/legend used. The categories represented by colors are of log-scale size, which to a human is not very intuitive – at least not when it comes to temperatures. Also, the two outer categories are joined, probably because of the few outliers but still the darkest red can be both 4.1°C or 12.9°C, which makes a hugh difference. Also as mentioned above, it does not offer an exact representation of the temperature.

So the conclusion: it’s a nice illustration to give a rough idea, but it lacks detail and when not read properly, it can be misinforming too.

Source: http://deredactie.be/cm/vrtnieuws/wetenschap/1.2576401

Birgit

This week, I came across a couple of visualisations that have to do with my hobby – sewing.

This visualisation gives an overview of all the different styles of necklines a garment can have. A stylised picture of every type is shown for visual understanding, accompanied by the correct term and a short explanation. This kind of visualisation also exist for dresses, skirts and trousers, as well as for undergarments – any kind of garment that has a specific name to indicate the style.

Visualisations like this are very powerful in the sense that it is much harder to explain such a style in just words. If someone refers to a certain type in an article, it is easy to know what they mean by taking the visualisation in front of you and looking it up. Combining the different visualisations (trousers, tops, skirts,…) can also be very helpful in the designing process of a new garment.

Carmen

In this week’s InfoVis I look into how not to visualize information, and more concretely how not to make powerpoints.

Life after Death by Powerpoint is a well-known video by Don McMillan. He teaches us how not to present information and use powerpoint.

The video shows him making a presentation supported by powerpoint. He points out all the wrong ways of presenting information and using powerpoint by actually doing them, sometimes in an over the top way to make his point even clearer. Some very valuable lessons can be taken from it.
He starts off with an overly crowded slide with a graph which is more like a maze than a graph. It is too complex and interwoven, and also too small to be readable by the audience.
Secondly, he points out how two graphs can be wrongly connected by comparing their shapes by showing the graph of “Number of Powerpoints by Day” and “Home Foreclosures by Day” and saying PowerPoint caused the mortgage meltdown. The lesson to learn from this one is to always make sure that you know what kind of information is being compared or used and take valid conclusions not just based on mere things like the shape of graphs.
Thirdly, he points out people tend to put everything they will say on PowerPoint slides. This makes your slides crowded, wordy and boring. The audience will loose attention rapidly. It is thus very important to always make a good selection of which information to represent. Bombarding your audience with information will not give good results.
McMillan also points out that font size and font type matter. The audience needs to be able to read your text to be able to interpret the information correctly.
Furthermore, he depicts that text should stay stationary to not distract the audience from the message, nor should there be too many animations. This can be extended to the lesson to limit unnecessary decorations, stick to the information that you have and that you want to represent.
Another common mistake is to make too many bulletpoints. Once again this shows to not give too much information in one view to not bombard the audience with information.
It is also important not to use too many abbreviations as those may hinder easy information interpretation.
Lastly, it is important to not use too many charts. Not all information is fit to put into charts nor should all information that is available be provided to the audience. Once again, make sure to only select relevant information and present this in a meaningful way.

During the second lecture we were asked to think of as many ways as possible to visualize a small dataset consisting out of two numbers, 75 and 37. In this post we will discuss the visualizations we came up with and some key takeaways from the exercice.

Discussion

There are different ways to represent numbers.

We can represent them symbolically as numbers, possibly scaled by its size (5).

We can represent them as a count. For example by turfing the number (2), or a certain amount of circles – grouped (9) or non-grouped (7) – or a full rectangle representing 10 and a circle representing a 1 (4),…

We can represent them in a more analytic way, as a bar chart (1) or a proportion of a circle (11, 14) or bar (3), the size of the circles (12) as a two-dimensional position (8) or on a one-dimensional scale (13).

We can also represent them as daily objects, such as a wine bottle representing 75 and a can plus shot glass representing 37 (amount of cl) (16), or with dices (15), as a temperature on a thermometer (6), as an amount of money (10).

Lastly, we also represented them as a binary number (17) and a hexadecimal number (18).

Key takeaways

There are a vast amount of ways to represent information. There is not one correct way of representing information. The most suitable way will be determined by the context of the information and the target audience. Different ways of representing will suit different goals.
When choosing a representation it is thus important to always take these 3 key elements into account: what is the data about, who is our target audience and what is our goal?

The data used were all votes from the US 2008 elections that were not cast on Obama or McCain. Every state has a pie chart that represents the division of these remaining votes. The size of the pie chart represents the share of the total number of votes cast in that state. The scale can be seen on the right of the map. Below the map is also a visualisation of the hypothetical electoral college.

A ‘lesson learned’ indicated by the designer himself is that on his visualisation, the percentages are linked to the diameter of the pie chart (as you can see on the scale), so an increase of one percent results in the doubling of the diameter. This causes differences to seem much larger than they actually are, or for some states, the pie charts to be nearly invisible.

Furthermore, there are several sources ([1],[2],[3]) that state that people in general are not very good at comparing angles and surfaces. This makes it hard to properly compare different areas in a pie chart. For charts with 2 or three elements, it is still somewhat possible – although the perception will still not be accurate – but from the moment there are four or more, it becomes nearly impossible.

So, even though it looks good from a design point of view, it is actually not a very good visualistion.

In this second part Glenn discusses the redesign of the subway map of Paris made by Max Roberts, teacher Psychology at the university of Essex.

Most maps nowadays map routes of public services on the real grid plan of the city. Although they are veracious, they tend to be inefficient. This raised the question, how to make them more efficient. According to Max Roberts it should be possible to get the most efficient route in one blink of an eye.

The production of this design wasn’t easy: Paris has one of the most extensive and confusing subway networks around the world. So before Robert started on his design he studied the archives of the RATP ( public services company of the french capital Paris) and the design of the la Goutte d’Or district.

After his thorough study of the zigzag patrones on those versions he decided to give the chart a cyclic structure. Getting rid of unneccesary stops and short cuts made it so that travelers could quickly pick the most efficient route, maybe not the shortest, but definitely the fastest.

According to Roberts, a subway map shouldn’t follow the grid of the city. The purpose of those maps isn’t to represent the distance between stops in an accurate way, but to visualise to connectivity between stops in an efficient way so travelers could pick the best option for them eventhough this means in reality travelling more kilometers. The results were that travelers would cut there travelling time in half. [4]

So it seems that when it comes to visualising information it is not always neccesary to represent it in a realistic way, but often even better to do it in an abstract way so we don’t get distracted by unneccasairy details and things that do not matter. This way the information is less cluttered with all kinds of stuff and most of the time more efficient in showing the relevant information. This vision is based on one of the principles of abstraction which is only show what is relevant at the given level of abstraction so things stay comprehensible and clear. [5]

I only have one minor issue with this representation and that is when your travelling throughout the city and you don’t know which station you need to take to get to your destination, this plan will be of no use. So if you want to use this plan, you should have an idea of the city layout and therefor be combining a city map and a subway map to get to your destination which is what nowadays subway maps do. This does not apply to regular subway users since they either know which stations to take or the city layout.
So for the sole purpose of representing the information of the connectivity of the subway stations it is good. However when occasional travellers are using it they also need a city map, be it an mental representation or a real map, so a combination of those two should always be present when planning a route. So it’s good to keep an eye on the application when visualising information.

In this third part Carmenlooks at a graph present in a previous course of hers (Integral quality management).

[6]

The graph shows the progress of costs and cash flow from the moment of conceptualization of the product over the entire life span. It is a theoretical visualization, it does not rely on actual data but upon some theoretical compounds of integral quality management.

The graph was present in the course book which was printed completely black and white. This was also the only place the graph was presented towards the students. As can be seen from the picture, there is a clear mismatch in design and presentation of the graph. The graph was designed with a color legend but is presented in a black and white book. The legend becomes futile and the students is left to guess or research which line respresents what.

This case clearly showes that it is very important to always think about the form in which your infographic will be presented. This problem could have been easily prevented by using full/dotted lines.

Carmen is 22 years old and studies Business Engineering: Management Information Systems. She is the president of Erasmus Student Network Leuven and likes to play tennis. She took the Information Visualisation course as part of her minor in Human-Computer Interaction.

Glenn is 21 years old and studies Master in Engineering Science: Computer Science. His hobby is archery and he spends part of his free time gaming. He thinks it is important that information is clearly represented to avoid ambiguity.

Birgit is 24 years old and studies Business Engineering: Management Information Systems. She is vice-president of Academics for Technology and likes to sew clothes. Just like Carmen, she took the course as part of her minor, because she feels it will be useful in her further career.

We’re looking forward to this journey and invite you to follow us along!